How to Handle Floating Point Precision Issues in a Custom Hash Function for C++ Unordered Map?
I'm working through a tutorial and I'm currently developing a caching mechanism in C++ that uses an `unordered_map` to store floating point values as keys..... However, I'm working with important precision issues when generating hash values for these floats. When I use the default hash on a float, I notice that nearly identical values are being treated as distinct keys, which is causing unexpected behavior in my cache. For instance: ```cpp #include <iostream> #include <unordered_map> #include <cmath> struct FloatHash { std::size_t operator()(float f) const { return std::hash<int>()(static_cast<int>(f * 1000)); // multiply to avoid precision issues } }; int main() { std::unordered_map<float, std::string, FloatHash> cache; cache[1.000f] = "One"; cache[1.0001f] = "One point zero zero one"; std::cout << "Cache size: " << cache.size() << '\n'; return 0; } ``` This code prints `Cache size: 2` even though the values I want to represent are very close. I multiplied the float by 1000 and cast it to an integer for the hash function, but I still see issues when I insert values like `1.0000001`. What is the best way to implement a hash function for floats that minimizes these precision issues? I'm using C++17 and would like any suggestions or best practices for handling this kind of scenario. Additionally, I'm curious if switching to `double` would alleviate this scenario or if it would introduce even more complexity. Any insights would be greatly appreciated! The stack includes C++ and several other technologies. What's the best practice here?